An Intelligent Risk-Aware AI and LLM Platform for Secure Banking Operations and Trade Safety Analytics in Cloud-Based Web Applications

Abstract

The rapid digitization of banking operations and trade systems has increased efficiency, accessibility, and global financial integration, but it has also amplified the risks of fraud, cyberattacks, and operational anomalies. Traditional detection systems are often static, reactive, and unable to handle high-velocity, multi-source data in real time. This paper proposes an intelligent risk-aware AI and Large Language Model (LLM) platform for secure banking operations and trade safety analytics within cloud-based web applications. The platform integrates generative and predictive AI models with LLMs to identify anomalies, forecast potential threats, and generate human-readable explanations and automated reports. Secure Extract, Transform, Load (ETL) pipelines ensure consistent, high-quality data from heterogeneous sources, while a risk-aware module dynamically evaluates threat severity, prioritizes mitigation, and adjusts system parameters. Cloud-native deployment enables scalable, low-latency, and fault-tolerant analytics suitable for high-speed web applications. Evaluation on real and simulated banking and trade datasets demonstrates improved detection accuracy (>95%), a 35–40% reduction in false positives, and enhanced operational resilience. This work provides a blueprint for deploying adaptive, interpretable, and secure AI platforms that integrate risk-awareness, cloud scalability, and 5G-ready web technologies to safeguard modern financial ecosystems.